# Importing necessary libraries
import pandas as pd
import numpy as np
import re
import matplotlib.pyplot as plt
import plotly.express as px
import seaborn as sns
from datetime import datetime,timedelta
from itertools import combinations, islice
from collections import Counter
import operator
import warnings
warnings.filterwarnings('ignore')
pd.set_option('display.max_columns', None)

df = pd.read_csv('netflix-rotten-tomatoes-metacritic-imdb.csv')

df.head()

df.shape

(15480, 29)

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 15480 entries, 0 to 15479
Data columns (total 29 columns):
 #   Column                 Non-Null Count  Dtype  
---  ------                 --------------  -----  
 0   Title                  15480 non-null  object 
 1   Genre                  13770 non-null  object 
 2   Tags                   15413 non-null  object 
 3   Languages              13545 non-null  object 
 4   Series or Movie        15480 non-null  object 
 5   Hidden Gem Score       13379 non-null  float64
 6   Country Availability   15461 non-null  object 
 7   Runtime                15479 non-null  object 
 8   Director               10772 non-null  object 
 9   Writer                 11150 non-null  object 
 10  Actors                 13555 non-null  object 
 11  View Rating            8456 non-null   object 
 12  IMDb Score             13381 non-null  float64
 13  Rotten Tomatoes Score  6382 non-null   float64
 14  Metacritic Score       4336 non-null   float64
 15  Awards Received        6075 non-null   float64
 16  Awards Nominated For   7661 non-null   float64
 17  Boxoffice              4007 non-null   object 
 18  Release Date           13373 non-null  object 
 19  Netflix Release Date   15480 non-null  object 
 20  Production House       5149 non-null   object 
 21  Netflix Link           15480 non-null  object 
 22  IMDb Link              13177 non-null  object 
 23  Summary                15471 non-null  object 
 24  IMDb Votes             13379 non-null  float64
 25  Image                  15480 non-null  object 
 26  Poster                 11842 non-null  object 
 27  TMDb Trailer           7194 non-null   object 
 28  Trailer Site           7194 non-null   object 
dtypes: float64(7), object(22)
memory usage: 3.4+ MB

df.describe().T

# Create a dataframe containing scores from the platforms scaling IMDB to 100
scores_df = pd.DataFrame({'imdb_scores': df['IMDb Score']*10,
                          'rt_scores': df['Rotten Tomatoes Score'],
                          'mc_scores': df['Metacritic Score']})

# Different Platfomrms Scores densities
plt.figure(figsize=(12,5), dpi=150)
sns.kdeplot(data=scores_df, x='imdb_scores', label='IMDb Score', fill=True)
sns.kdeplot(data=scores_df, x='rt_scores', label= 'Rotten Tomatoes Score', fill=True)
sns.kdeplot(data=scores_df, x='mc_scores', label= 'Metacritic Score', fill=True)
plt.legend(loc=('upper left'))
plt.xlim(0,100)
plt.title('IMDb vs Rotten Tomatoes vs Metacritic score densities')
plt.xlabel('Score %')
plt.show()

selected_columns = ['Title', 'Genre', 'Languages', 'Series or Movie', 'Hidden Gem Score', 'Runtime',
                    'Director', 'Actors', 'IMDb Score', 'IMDb Votes', 'Awards Nominated For', 'Awards Received',
                    'Boxoffice', 'Netflix Release Date', 'Country Availability']
df_selected = df[selected_columns].copy()

df_selected.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 15480 entries, 0 to 15479
Data columns (total 15 columns):
 #   Column                Non-Null Count  Dtype  
---  ------                --------------  -----  
 0   Title                 15480 non-null  object 
 1   Genre                 13770 non-null  object 
 2   Languages             13545 non-null  object 
 3   Series or Movie       15480 non-null  object 
 4   Hidden Gem Score      13379 non-null  float64
 5   Runtime               15479 non-null  object 
 6   Director              10772 non-null  object 
 7   Actors                13555 non-null  object 
 8   IMDb Score            13381 non-null  float64
 9   IMDb Votes            13379 non-null  float64
 10  Awards Nominated For  7661 non-null   float64
 11  Awards Received       6075 non-null   float64
 12  Boxoffice             4007 non-null   object 
 13  Netflix Release Date  15480 non-null  object 
 14  Country Availability  15461 non-null  object 
dtypes: float64(5), object(10)
memory usage: 1.8+ MB

df_selected.shape

(15480, 15)

# Convert the 'Netflix Release Date' column to a datetime format
df_selected['Netflix Release Date'] = pd.to_datetime(df_selected['Netflix Release Date'])

# Extract year and month info
df_selected['Year'] = df_selected['Netflix Release Date'].dt.year
df_selected['Month'] = df_selected['Netflix Release Date'].dt.month

df_selected.dtypes

Title                           object
Genre                           object
Languages                       object
Series or Movie                 object
Hidden Gem Score               float64
Runtime                         object
Director                        object
Actors                          object
IMDb Score                     float64
IMDb Votes                     float64
Awards Nominated For           float64
Awards Received                float64
Boxoffice                       object
Netflix Release Date    datetime64[ns]
Country Availability            object
Year                             int64
Month                            int64
dtype: object

df_gen = df_selected.copy()
# Drop rows with missing values in the 'Genre', 'IMDb Score', and 'IMDb Votes' columns
df_gen.dropna(subset=['Genre','IMDb Score','IMDb Votes'],inplace=True)

# Manipulate 'Genre' column and explode into separate rows
df_gen['Genre'] = df['Genre'].astype("string")
df_gen['Genre']=df_gen['Genre'].map(lambda x:x.split(','))
df_gen =df_gen.explode('Genre').reset_index(drop=True)
df_gen['Genre'] = df_gen['Genre'].str.strip()

# Filter the dataframe to include only rows with 'IMDb Score' greater than the mean 'IMDb Score'
df_gen = df_gen[df_gen['IMDb Score'] > df_gen['IMDb Score'].mean()]

# Create a new column 'mul_rating' as a weighted average of 'IMDb Score' and 'IMDb Votes'
df_gen['mul_rating'] =((0.2*df_gen['IMDb Score'] + 0.8*df_gen['IMDb Votes'])/((0.2*df_gen['IMDb Score'] + 0.8*df_gen['IMDb Votes']).max()))*100

df_gen.describe().T

# Group the dataframe by 'Genre' and compute average 'IMDb Score', total 'IMDb Votes', and mean 'mul_rating'
df_gen_plot = df_gen.groupby('Genre').agg({'IMDb Score': 'mean',
                                           'IMDb Votes': 'sum',
                                           'mul_rating': 'mean'}).reset_index().sort_values('mul_rating', ascending=False)
# Create a copy of the dataframe with min-max normalized 'mul_rating'
normalized_df = df_gen_plot.copy()
normalized_df['mul_rating'] = (df_gen_plot['mul_rating'] - df_gen_plot['mul_rating'].min()) / (df_gen_plot['mul_rating'].max() - df_gen_plot['mul_rating'].min()) * 10

# Create a bar plot of the normalized ratings for each genre
plt.figure(figsize=(12,5), dpi=150)
sns.barplot(data= normalized_df, x='Genre', y='mul_rating', palette='rocket')
plt.title('Genres Rating')
plt.ylabel('Rating')
plt.xlabel('Genre')
plt.xticks(rotation = 45, ha='right')
plt.show()

# Grouping the data by year and genre to get the number of titles per genre per year
genre_per_year = df_gen.groupby(['Year', 'Genre'])['Year'].count().reset_index(name='Count of Genres per Year')

# Sorting the data by year and count of genres per year in descending order
genre_per_year = genre_per_year.sort_values(['Year', 'Count of Genres per Year'], ascending=False)

# Selecting the data for years between 2016 and 2020
genre_per_year = genre_per_year[(genre_per_year['Year'] >= 2016) & (genre_per_year['Year'] <= 2020)]

# Selecting the top 5 genres for each year
top_genres_per_year = genre_per_year.groupby('Year').head(5).reset_index(drop=True)

# Creating a bar plot to show the top 5 genres for each year
plt.figure(figsize=(12, 5))
palette = 'flare'
sns.barplot(data=top_genres_per_year, x='Year', y='Count of Genres per Year', hue='Genre', palette=palette, dodge=False)
plt.ylim(0, 900)
plt.title('Top 5 Genres per Year')
plt.xlabel('Year')
plt.ylabel('Number of Titles')
plt.legend(loc='center right', bbox_to_anchor=(1.3, 0.5))
plt.tight_layout()
plt.show()

# Group the data by year and series/movie
df_prod = df_selected.groupby(['Year', 'Series or Movie']).agg({'Title': 'count'}).reset_index()

# Filter the data to only include years between 2016 and 2020
df_prod = df_prod[(df_prod['Year'] >= 2016) & (df_prod['Year'] <= 2020)]

# Creating a bar plot to show the number of Movie and Series titles each year

sns.barplot(data=df_prod, x="Year", y="Title", hue="Series or Movie", palette='rocket')
plt.title('Number of Netflix Titles Released per Year')
plt.xlabel('Year')
plt.ylabel('Number of Titles')
plt.legend(title='', loc='upper left')
plt.show()

# Select relevant columns and rows with valid actors
df_act = df_selected[['Title', 'Actors']].dropna()
df_act['Actors'] = df_act['Actors'].astype(str).str.split(',')

# Create list of actor pairs and count occurrences using Counter
comblist = []
for actors in df_act['Actors']:
    comb = combinations(actors, 2)
    for pair in comb:
        comblist.append(pair)
c = Counter(comblist)

# Get top 15 pairs and plot a scatter plot
pairs = c.most_common(15)
pairs1 = np.array(pairs)
lispa1 = list(map(lambda x: list(x[0]), pairs))
lispa2 = list(map(lambda x: x[1], pairs))
b = list(map(lambda x: '-'.join(x), lispa1))

#Counting the number of times pairs of actors have appeared in movies together
plt.figure(figsize=(12, 5), dpi=150)
plt.scatter(b, lispa2, alpha=0.8, s=100, edgecolors='black', c='firebrick')
plt.xticks(rotation=45, ha='right')
plt.title('Most Dynamic Acting Duos', fontsize=16)
plt.xlabel('Acting Duos', fontsize=14)
plt.ylabel('Number of Movies', fontsize=14)
plt.tight_layout()
plt.show()

# Extract directors data from the DataFrame
df_dir = df[df['Director'].notna()]
df_dir['Director'] = df_dir['Director'].astype("string")
df_dir['Director']=df_dir['Director'].map(lambda x:x.split(','))
df_dir = df_dir.explode('Director').reset_index(drop=True)

# Count the number of movies each director has directed
df_dir['Director_Count'] = df_dir['Director'].map(df_dir['Director'].value_counts())

# Aggregate the data based on director and calculate relevant metrics
df_dir1 = df_dir.groupby('Director').agg({'Hidden Gem Score':np.mean,
                                          'IMDb Score':np.mean,
                                          'IMDb Votes':np.sum,
                                          'Director_Count':pd.Series.count,
                                          'Awards Received':np.sum}).reset_index()

# Filter the data to only include directors with above-average IMDb Votes, Director_Count, and Awards Received, and sort by IMDb Score
df_dir2 = df_dir1[(df_dir1['IMDb Votes'] > df_dir1['IMDb Votes'].mean())
       & (df_dir1['Director_Count'] > df_dir1['Director_Count'].mean())
       & (df_dir1['Awards Received'] > df_dir1['Awards Received'].mean())].sort_values(['IMDb Score',
                                                                                        'IMDb Votes'], ascending=False)[['Director','IMDb Score']].head(10)

# Plot a scatterplot of IMDb Score vs. Director for the top 10 directors
plt.figure(figsize=(12, 5), dpi=150)
plt.scatter(x=df_dir2['Director'], y=df_dir2['IMDb Score'], alpha=0.8, s=100, edgecolors='black', c='red')
plt.xticks(rotation=45, ha='right', fontsize=12)
plt.yticks(fontsize=12)
plt.title('Top Directors according to IMDb Score', fontsize=16, color='white')
plt.xlabel('Director', fontsize=14)
plt.ylabel('IMDb Score', fontsize=14)
plt.tight_layout()
plt.show()

fig = px.bar(df_dir2, x='Director', y='IMDb Score', color='IMDb Score', 
             color_continuous_scale=px.colors.sequential.Reds)
fig.update_layout(title='Top Directors according to IMDb Score', 
                  xaxis_title='Director', yaxis_title='IMDb Score',
                  xaxis_tickangle=-45)
fig.show()

# Creating a dataframe to search for genre diverse actors

df_act = df[df['Actors'].notna()]
df_act['Actors'] = df_act['Actors'].astype("string")
df_act['Actors']=df_act['Actors'].map(lambda x:x.split(','))
df_act=df_act.explode('Actors').reset_index(drop=True)
df_act
df_act = df_act[df_act['Genre'].notna()]
df_act['Genre'] = df_act['Genre'].astype("string")
df_act['Genre']=df_act['Genre'].map(lambda x:x.split(','))
df_act=df_act.explode('Genre').reset_index(drop=True)
df_act['Genre'] = df_act['Genre'].str.strip()
df_act.head()

#Top Genre-Diverse Actors
df_act2=df_act.groupby('Actors').nunique()[['Genre','Title']].reset_index()
df_act_div = df_act2.sort_values('Genre',ascending=False).head(10)

sns.barplot(data=df_act_div, x="Actors", y="Genre", palette='flare')
plt.title('Top Genre-Diverse Actors')
plt.xticks(rotation = 45, ha='right')
plt.xlabel('Actors')
plt.ylabel('Genres')
plt.show()

# Top genre-specific actors

df_act2['Difference'] = df_act2['Title']-df_act2['Genre']
df_act_spec = df_act2.sort_values('Difference',ascending=False).head(10)

#Plot a barplot for Genre-Specific Actors

f, ax = plt.subplots(figsize=(6, 6))

# Plot the total titles for each actor
sns.set_color_codes("pastel")
sns.barplot(x="Title", y="Actors", data=df_act_spec,
            label="Title", color='r')

# Plot the corresponding genres
sns.set_color_codes("muted")
sns.barplot(x="Genre", y="Actors", data=df_act_spec,
            label="Genre", color='r')

ax.legend(ncol=2, loc="lower right", frameon=True)
ax.set(xlim=(0, 24), ylabel="",
       xlabel="Top Genre-Specific Actors")
sns.despine(left=True, bottom=True)

# Creating a dataframe for Box Office

df_boxoffice = df_gen.dropna(subset='Boxoffice')

df_boxoffice['Boxoffice'] = df_boxoffice['Boxoffice'].replace(r'[\$,]','',regex=True)
df_boxoffice['Boxoffice'] = pd.to_numeric(df_boxoffice['Boxoffice'])

# Creating two dataframes for Series and Movies Box Office

df_boxoffice_series = df_boxoffice[df_boxoffice['Series or Movie'] == 'Series'].groupby(['Genre']).agg({'Boxoffice' : np.sum}).sort_values('Boxoffice', ascending=False).reset_index().head(10)
df_boxoffice_movies = df_boxoffice[df_boxoffice['Series or Movie'] == 'Movie'].groupby(['Genre']).agg({'Boxoffice' : np.sum}).sort_values('Boxoffice', ascending=False).reset_index().head(10)

# Plot a Barplot for Series Box Office by Genre

sns.barplot(data=df_boxoffice_series, x="Genre", y="Boxoffice", palette='flare')
plt.xticks(rotation = 45, ha='right')
plt.title('Series Box Office by Genre')
plt.ylabel('Box Office in million $')
plt.show()

# Plot a Barplot for Movies Box Office by Genre

sns.barplot(data=df_boxoffice_movies, x="Genre", y="Boxoffice", palette='flare')
plt.xticks(rotation = 45, ha='right')
plt.title('Movies Box Office by Genre')
plt.ylabel('Box Office in 10 milion $')
plt.savefig('figure1.png', transparent=True,bbox_inches='tight') # save as png

plt.show()

	Title	Genre	Tags	Languages	Series or Movie	Hidden Gem Score	Country Availability	Runtime	Director	Writer	Actors	View Rating	IMDb Score	Rotten Tomatoes Score	Metacritic Score	Awards Received	Awards Nominated For	Boxoffice	Release Date	Netflix Release Date	Production House	Netflix Link	IMDb Link	Summary	IMDb Votes	Image	Poster	TMDb Trailer	Trailer Site
0	Lets Fight Ghost	Crime, Drama, Fantasy, Horror, Romance	Comedy Programmes,Romantic TV Comedies,Horror ...	Swedish, Spanish	Series	4.3	Thailand	< 30 minutes	Tomas Alfredson	John Ajvide Lindqvist	Kåre Hedebrant, Per Ragnar, Lina Leandersson, ...	R	7.9	98.0	82.0	74.0	57.0	$2,122,065	12 Dec 2008	2021-03-04	Canal+, Sandrew Metronome	https://www.netflix.com/watch/81415947	https://www.imdb.com/title/tt1139797	A med student with a supernatural gift tries t...	205926.0	https://occ-0-4708-64.1.nflxso.net/dnm/api/v6/...	https://m.media-amazon.com/images/M/MV5BOWM4NT...	NaN	NaN
1	HOW TO BUILD A GIRL	Comedy	Dramas,Comedies,Films Based on Books,British	English	Movie	7.0	Canada	1-2 hour	Coky Giedroyc	Caitlin Moran	Paddy Considine, Cleo, Beanie Feldstein, Dónal...	R	5.8	79.0	69.0	1.0	NaN	$70,632	08 May 2020	2021-03-04	Film 4, Monumental Pictures, Lionsgate	https://www.netflix.com/watch/81041267	https://www.imdb.com/title/tt4193072	When nerdy Johanna moves to London, things get...	2838.0	https://occ-0-1081-999.1.nflxso.net/dnm/api/v6...	https://m.media-amazon.com/images/M/MV5BZGUyN2...	https://www.youtube.com/watch?v=eIbcxPy4okQ	YouTube
2	Centigrade	Drama, Thriller	Thrillers	English	Movie	6.4	Canada	1-2 hour	Brendan Walsh	Brendan Walsh, Daley Nixon	Genesis Rodriguez, Vincent Piazza	Unrated	4.3	NaN	46.0	NaN	NaN	$16,263	28 Aug 2020	2021-03-04	NaN	https://www.netflix.com/watch/81305978	https://www.imdb.com/title/tt8945942	Trapped in a frozen car during a blizzard, a p...	1720.0	https://occ-0-1081-999.1.nflxso.net/dnm/api/v6...	https://m.media-amazon.com/images/M/MV5BODM2MD...	https://www.youtube.com/watch?v=0RvV7TNUlkQ	YouTube
3	ANNE+	Drama	TV Dramas,Romantic TV Dramas,Dutch TV Shows	Turkish	Series	7.7	Belgium,Netherlands	< 30 minutes	NaN	NaN	Vahide Perçin, Gonca Vuslateri, Cansu Dere, Be...	NaN	6.5	NaN	NaN	1.0	NaN	NaN	01 Oct 2016	2021-03-04	NaN	https://www.netflix.com/watch/81336456	https://www.imdb.com/title/tt6132758	Upon moving into a new place, a 20-something r...	1147.0	https://occ-0-1489-1490.1.nflxso.net/dnm/api/v...	https://m.media-amazon.com/images/M/MV5BNWRkMz...	NaN	NaN
4	Moxie	Animation, Short, Drama	Social Issue Dramas,Teen Movies,Dramas,Comedie...	English	Movie	8.1	Lithuania,Poland,France,Iceland,Italy,Spain,Gr...	1-2 hour	Stephen Irwin	NaN	Ragga Gudrun	NaN	6.3	NaN	NaN	NaN	4.0	NaN	22 Sep 2011	2021-03-04	NaN	https://www.netflix.com/watch/81078393	https://www.imdb.com/title/tt2023611	Inspired by her moms rebellious past and a con...	63.0	https://occ-0-4039-1500.1.nflxso.net/dnm/api/v...	https://m.media-amazon.com/images/M/MV5BODYyNW...	NaN	NaN

	count	mean	std	min	25%	50%	75%	max
Hidden Gem Score	13379.0	5.937551	2.250202	0.6	3.8	6.8	7.9	9.8
IMDb Score	13381.0	6.496054	1.146910	1.0	5.8	6.6	7.3	9.7
Rotten Tomatoes Score	6382.0	59.523034	26.999173	0.0	38.0	64.0	83.0	100.0
Metacritic Score	4336.0	56.813653	17.582545	5.0	44.0	57.0	70.0	100.0
Awards Received	6075.0	8.764444	18.311171	1.0	1.0	3.0	8.0	300.0
Awards Nominated For	7661.0	13.983161	29.821052	1.0	2.0	5.0	12.0	386.0
IMDb Votes	13379.0	42728.411615	125701.191329	5.0	403.5	2322.0	20890.5	2354197.0

	count	mean	std	min	25%	50%	75%	max
Hidden Gem Score	18689.0	5.884280	2.162107	1.700000	3.900000	5.80000	8.100000	9.8
IMDb Score	18689.0	7.381791	0.567072	6.600000	6.900000	7.30000	7.800000	9.7
IMDb Votes	18689.0	71812.519557	168118.728762	5.000000	991.000000	5753.00000	59010.000000	2354197.0
Awards Nominated For	12899.0	20.844872	38.008842	1.000000	3.000000	8.00000	21.000000	386.0
Awards Received	11022.0	12.107149	22.572374	1.000000	2.000000	5.00000	12.000000	300.0
Year	18689.0	2017.387394	1.976495	2015.000000	2015.000000	2017.00000	2019.000000	2021.0
Month	18689.0	6.496228	3.430340	1.000000	4.000000	6.00000	10.000000	12.0
mul_rating	18689.0	3.050479	7.141229	0.000282	0.042165	0.24445	2.506655	100.0

	Title	Genre	Tags	Languages	Series or Movie	Hidden Gem Score	Country Availability	Runtime	Director	Writer	Actors	View Rating	IMDb Score	Rotten Tomatoes Score	Metacritic Score	Awards Received	Awards Nominated For	Boxoffice	Release Date	Netflix Release Date	Production House	Netflix Link	IMDb Link	Summary	IMDb Votes	Image	Poster	TMDb Trailer	Trailer Site
0	Lets Fight Ghost	Crime	Comedy Programmes,Romantic TV Comedies,Horror ...	Swedish, Spanish	Series	4.3	Thailand	< 30 minutes	Tomas Alfredson	John Ajvide Lindqvist	Kåre Hedebrant	R	7.9	98.0	82.0	74.0	57.0	$2,122,065	12 Dec 2008	2021-03-04	Canal+, Sandrew Metronome	https://www.netflix.com/watch/81415947	https://www.imdb.com/title/tt1139797	A med student with a supernatural gift tries t...	205926.0	https://occ-0-4708-64.1.nflxso.net/dnm/api/v6/...	https://m.media-amazon.com/images/M/MV5BOWM4NT...	NaN	NaN
1	Lets Fight Ghost	Drama	Comedy Programmes,Romantic TV Comedies,Horror ...	Swedish, Spanish	Series	4.3	Thailand	< 30 minutes	Tomas Alfredson	John Ajvide Lindqvist	Kåre Hedebrant	R	7.9	98.0	82.0	74.0	57.0	$2,122,065	12 Dec 2008	2021-03-04	Canal+, Sandrew Metronome	https://www.netflix.com/watch/81415947	https://www.imdb.com/title/tt1139797	A med student with a supernatural gift tries t...	205926.0	https://occ-0-4708-64.1.nflxso.net/dnm/api/v6/...	https://m.media-amazon.com/images/M/MV5BOWM4NT...	NaN	NaN
2	Lets Fight Ghost	Fantasy	Comedy Programmes,Romantic TV Comedies,Horror ...	Swedish, Spanish	Series	4.3	Thailand	< 30 minutes	Tomas Alfredson	John Ajvide Lindqvist	Kåre Hedebrant	R	7.9	98.0	82.0	74.0	57.0	$2,122,065	12 Dec 2008	2021-03-04	Canal+, Sandrew Metronome	https://www.netflix.com/watch/81415947	https://www.imdb.com/title/tt1139797	A med student with a supernatural gift tries t...	205926.0	https://occ-0-4708-64.1.nflxso.net/dnm/api/v6/...	https://m.media-amazon.com/images/M/MV5BOWM4NT...	NaN	NaN
3	Lets Fight Ghost	Horror	Comedy Programmes,Romantic TV Comedies,Horror ...	Swedish, Spanish	Series	4.3	Thailand	< 30 minutes	Tomas Alfredson	John Ajvide Lindqvist	Kåre Hedebrant	R	7.9	98.0	82.0	74.0	57.0	$2,122,065	12 Dec 2008	2021-03-04	Canal+, Sandrew Metronome	https://www.netflix.com/watch/81415947	https://www.imdb.com/title/tt1139797	A med student with a supernatural gift tries t...	205926.0	https://occ-0-4708-64.1.nflxso.net/dnm/api/v6/...	https://m.media-amazon.com/images/M/MV5BOWM4NT...	NaN	NaN
4	Lets Fight Ghost	Romance	Comedy Programmes,Romantic TV Comedies,Horror ...	Swedish, Spanish	Series	4.3	Thailand	< 30 minutes	Tomas Alfredson	John Ajvide Lindqvist	Kåre Hedebrant	R	7.9	98.0	82.0	74.0	57.0	$2,122,065	12 Dec 2008	2021-03-04	Canal+, Sandrew Metronome	https://www.netflix.com/watch/81415947	https://www.imdb.com/title/tt1139797	A med student with a supernatural gift tries t...	205926.0	https://occ-0-4708-64.1.nflxso.net/dnm/api/v6/...	https://m.media-amazon.com/images/M/MV5BOWM4NT...	NaN	NaN

Netflix EDA¶

Top Genres¶

Popular Genres per Year¶

Yearly series and movies production¶

Acting Duos¶

Top Directors¶

Top Genre-Diverse Actors¶

Top Genre-Specific Actors¶

Box Office¶